潜在扩散模型驱动的航空时序图像生成方法

商钰滢; 侯英妍; 刘子楠; 卢宛萱; 黄宇鸿; 王逸潇; 于泓峰; 付琨

doi:10.11999/JEIT260165

潜在扩散模型驱动的航空时序图像生成方法

doi: 10.11999/JEIT260165 cstr: 32379.14.JEIT260165

商钰滢^{1, 2, 3, 4},
侯英妍^{1, 2, 3, 4, ,},
刘子楠^{1, 2, 3},
卢宛萱^{1, 4},
黄宇鸿^{5, 1, 3, 4},
王逸潇^{1, 2, 3, 4},
于泓峰^{1, 4},
付琨^{1, 3, 4}

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院大学电子电气与通信工程学院北京 100190
3.
中国科学院大学北京 100190
4.
中国科学院空天信息创新研究院，目标认知与应用技术重点实验室北京 100094
5.
中国科学院大学前沿交叉科学学院北京 100190

详细信息

作者简介:
商钰滢：女，博士生，研究方向为视觉语言理解，多模态语义对齐

侯英妍：女，博士生，研究方向为遥感图像解译与生成

刘子楠：男，博士生，研究方向为SAR图像三维重建

卢宛萱：女，副研究员，研究方向为遥感图像解译

黄宇鸿：女，博士生，研究方向为遥感图像解译

王逸潇：男，研究员，研究方向为遥感图像解译

于泓峰：男，副研究员，研究方向为遥感图像解译

付琨：男，研究员，研究方向为遥感图像解译，地理空间大数据挖掘

通讯作者:
侯英妍　houyy@aircas.ac.cn

中图分类号: TP391/TP183
计量
- 文章访问数: 16
- HTML全文浏览量: 4
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2026-02-06
- 修回日期: 2026-03-09
- 录用日期: 2026-04-09
- 网络出版日期: 2026-04-13

Aerial Spatio-Temporal Image Generation via Latent Diffusion Model

SHANG Yuying^{1, 2, 3, 4},
HOU Yingyan^{1, 2, 3, 4
, ,},
LIU Zinan^{1, 2, 3},
LU Wanxuan^{1, 4},
HUANG Yuhong^{5, 1, 3, 4},
WANG Yixiao^{1, 2, 3, 4},
YU Hongfeng^{1, 4},
FU Kun^{1, 3, 4}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China
3.
University of Chinese Academy of Sciences, Beijing, 100190, China
4.
The Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
5.
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China

摘要

摘要: 航空对地观测在环境监测、灾害预警和城市规划等领域发挥着关键作用。然而，受限于飞行平台续航能力、任务窗口时效性等现实约束，所获取的航空图像往往难以完整刻画地表的长期演化过程。尽管预训练扩散模型在图像生成领域展现出巨大潜力，但其在航空领域的应用仍面临严峻挑战。一方面，航空观测易受天气及飞行条件制约，高质量的长时序标注样本获取困难，难以支撑模型的有效训练；另一方面，航空平台飞行高度灵活多变，地物尺度跨度大，使得文本描述语义难以与多尺度视觉特征精确匹配，进而引发语义-视觉对齐偏差问题。为应对上述挑战，本文提出了一个用于航空时序图像生成的免训练框架(ASTIG)。该框架首先利用视觉语言模型构建航空时序地表演变描述，并引入动态语义分解过程将复杂的时序描述转化为帧级视觉提示，为推理阶段提供细粒度的语义引导。随后，创新性地提出了一种语言绑定策略，在扩散模型的交叉注意力机制中建立文本中关键地物与对应视觉属性的显式关联，从而增强模型对语义特征的捕捉能力。最后，集成时序锚点注意力机制，利用双参考帧约束确保主体与背景在帧间的时序一致性，有效抑制帧间的时序内容偏移问题。定性和定量实验结果表明，本方法在时序连续性及语义保真度上均优于基线模型，为航空时序图像生成提供了新范式。
- 航空时序图像 /
- 扩散模型 /
- 文本图像生成 /
- 大语言模型 /
- 免训练生成
Abstract: Objective Aerial Earth observation plays a pivotal role in environmental monitoring, disaster warning, and urban planning. However, constrained by flight platform endurance, mission window timeliness, and other operational limitations, the acquired aerial imagery often fails to fully characterize the long-term evolutionary processes of the Earth's surface. Although pre-trained diffusion models have demonstrated considerable potential in image generation, their application in the aerial domain remains challenging due to the scarcity of high-quality temporal annotation data and the semantic–visual misalignment arising from variable observation scales. To address these challenges, this paper proposes ASTIG, a training-free framework for Aerial Spatio-Temporal Image Generation. By leveraging the generative priors of pre-trained latent diffusion models and large language models, ASTIG establishes a novel paradigm for semantically controllable aerial temporal image generation. Methods ASTIG comprises three synergistically designed components. First, a dynamic semantic decomposition process is introduced to automatically parse complex aerial scene evolution descriptions into frame-level visual prompts, compensating for the lack of temporal semantic annotations in existing aerial image-text datasets. Second, a linguistic binding strategy is proposed to establish explicit associations between key ground objects and their corresponding visual attributes within the cross-attention mechanism of the diffusion model, thereby enhancing the semantic response precision of generated imagery. Third, a temporal anchor attention mechanism is integrated, which employs dual reference frames to enforce subject stability and background consistency across the generated temporal images, effectively suppressing inter-frame temporal drift under training-free conditions. Results and Discussions ASTIG and baseline models are evaluated on 7, 236 high-quality aerial spatio-temporal descriptions across six automated metrics, including subject consistency, background consistency, temporal flickering, motion smoothness, aesthetic quality, and imaging quality. Quantitative results (Tables 1 and 2) demonstrate the superiority of ASTIG in temporal image generation, with improvements of 3. 91% and 4. 57% in subject consistency and temporal smoothness over the frame-prompt baseline, respectively. Qualitative comparisons (Fig. 4) further highlight its robust capability in modeling long-term geospatial evolutionary imagery. Ablation studies validate the individual effectiveness of the linguistic binding strategy and the temporal anchor attention mechanism (Table 3 and Fig. 5). Sensitivity analyses of the intervention steps (Table 4 and Fig. 6) and binding strength (Table 5 and Fig. 7) provide systematic exploration of optimal parameter configurations. Furthermore, extensibility experiments under satellite perspectives (Figs. 8 and 9) reveal the potential of ASTIG to generalize beyond aerial platforms to broader Earth observation scenarios. Conclusions This paper proposes ASTIG, a training-free framework for aerial spatio-temporal image generation that addresses the scarcity of high-quality long-term temporal data and semantic–visual misalignment. By leveraging the generative priors of pre-trained latent diffusion models and large language models, ASTIG integrates dynamic semantic decomposition process, linguistic binding strategy, and temporal anchor attention mechanism to jointly address temporal semantic construction, semantic response precision, and inter-frame consistency. Experimental results demonstrate that ASTIG outperforms existing baseline methods across multiple automated evaluation metrics, offering a novel paradigm for aerial spatio-temporal image generation. As a training-free method, the performance of ASTIG is inherently bounded by the prior knowledge of the backbone model. Future work will explore geometric correction and nadir-view prior constraints to better align generated results with the physical properties of satellite imagery.
- Aerial spatio-temporal image /
- diffusion models /
- text-to-image generation /
- large language models /
- training-free generation

HTML全文

图 1 航空时序图像生成的两大核心难点

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图 2 ASTIG框架图

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图 3 航空时序描述数据的收集流程

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图 4 基于“夏秋季节过渡：岩石半岛延伸入海，顶端可见圆形平顶建筑”文本提示的各方法定性对比结果

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图 5 针对“黄昏雪景：网格状建筑群，标志性建筑展现出金色穹顶及古典建筑元素”文本提示的各模块定性消融实验结果

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图 6 针对“悬崖海浪活动”文本提示的干预步数$ {N}_{inv} $定性消融实验结果

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图 7 针对“日落时分密集城市天际线”文本提示的绑定强度$ \eta $定性消融结果

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图 8 卫星视角下ASTIG与Text2Earth的文本驱动图像生成对比结果

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图 9 卫星视角下ASTIG对多种地表变化场景的时序图像生成结果

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表 1 单提示设置下本文方法与基线方法的定量对比结果

方法	SC	BC	TF	MS	IQ	AQ
LaVie^[19]	94.87	96.31	95.12	96.89	63.15	50.23
AnimateLCM^[34]	96.13	97.68	94.07	95.94	53.48	52.87
CogVideoX^[20]	95.58	97.48	98.54	98.76	49.57	48.83
Videoelevator (LaVie)^[35]	92.34	95.18	93.21	93.45	65.53	60.18
Videoelevator (AnimateLCM)^[35]	93.96	96.23	92.43	93.57	57.14	58.23
Text2Video-Zero^[5]	89.55	93.89	85.03	87.64	74.82	51.23
$ {\text{Free-Bloom}}_{\text{s}} $^[22]	96.68	97.13	93.89	94.21	77.19	61.36
$ {\text{ASTIG}}_{\text{s}} $	97.01	97.34	96.47	97.24	78.03	61.72

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表 2 帧提示设置下本文方法与基线方法的定量对比结果

方法	SC	BC	TF	MS	IQ	AQ
Free-Bloom	92.47	94.39	92.14	93.38	76.58	62.37
ASTIG	96.38	95.87	96.71	96.02	77.03	62.80

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表 3 各模块消融定量实验结果

方法	SC	BC	TF	MS	IQ	AQ
ASTIG	96.02	95.87	96.71	96.02	77.03	62.80
w/o LB	95.53	95.67	95.84	95.12	75.21	57.58
w/o TAA	72.15	85.18	86.84	88.67	76.82	56.23
w/o $ \text{At}{\mathrm{t}}_{\text{former}} $	96.38	95.83	96.38	95.74	76.98	61.36
w/o $ {\text{Att}}_{\text{first}} $	89.21	91.58	94.72	96.53	75.98	59.78

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表 4 干预步数$ {N}_{inv} $ 的消融定量实验结果

$ {N}_{inv} $	SC	BC	TF	MS	IQ	AQ
1	96.38	95.87	96.71	96.02	77.03	62.80
2	96.78	97.56	96.41	97.26	76.86	59.65
3	97.41	97.86	96.81	97.56	75.66	58.77
4	97.84	97.91	97.31	97.89	74.64	56.46

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表 5 绑定强度 $ \eta $的消融定量实验结果

	SC	BC	TF	MS	IQ	AQ
0	96.43	95.47	95.42	96.81	76.03	60.09
30	96.58	95.28	96.54	97.33	76.92	61.93
50	96.38	95.87	96.71	96.02	77.54	62.80
70	96.52	96.39	95.56	97.67	77.31	61.75
100	95.97	96.28	95.43	97.02	76.35	61.86

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